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020 _a9781394188499
020 _a9781394188536
_qelectronic book
020 _a1394188536
_qelectronic book
020 _a9781394188529
_qelectronic book
020 _a1394188528
_qelectronic book
020 _a9781394188505
_qelectronic book
020 _a1394188501
_qelectronic book
020 _z9781394188499
_qhardcover
024 7 _a10.1002/9781394188536
_2doi
035 _a(OCoLC)1405190907
037 _a10325561
_bIEEE
040 _aDLC
_beng
_erda
_cDLC
_dOCLCF
_dYDX
_dOCLCO
_dIEEEE
041 _aeng
042 _apcc
050 0 4 _aQA267
_b.Z43 2024
082 0 0 _a629.8/92631
_223/eng/20231018
100 1 _aZhang, JunQi
_c(Professor),
_0https://id.loc.gov/authorities/names/no2023099798
_eauthor.
245 1 0 _aLearning automata and their applications to intelligent systems /
_cJunQi Zhang, MengChu Zhou.
264 1 _aHoboken, New Jersey :
_bJohn Wiley & Sons, Inc.,
_c[2024]
300 _a1 online resource (xvii, 251 pages) :
_billustrations (chiefly color)
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
340 _2rdacc
_0http://rdaregistry.info/termList/RDAColourContent/1003.
504 _aIncludes bibliographical references and index.
505 0 _aAbout the Authors ix -- Preface xi -- Acknowledgments xiii -- A Guide to Reading this Book xv -- Organization of the Book xvii -- 1 Introduction 1 -- 1.1 Ranking and Selection in Noisy Optimization 2 -- 1.2 Learning Automata and Ordinal Optimization 5 -- 1.3 Exercises 7 -- 2 Learning Automata 9 -- 2.1 Environment and Automaton 9 -- 2.1.1 Environment 9 -- 2.1.2 Automaton 10 -- 2.1.3 Deterministic and Stochastic Automata 11 -- 2.1.4 Measured Norms 15 -- 2.2 Fixed Structure Learning Automata 16 -- 2.2.1 Tsetlin Learning Automaton 16 -- 2.2.2 Krinsky Learning Automaton 18 -- 2.2.3 Krylov Learning Automaton 19 -- 2.2.4 IJA Learning Automaton 20 -- 2.3 Variable Structure Learning Automata 21 -- 2.3.1 Estimator-Free Learning Automaton 22 -- 2.3.2 Deterministic Estimator Learning Automaton 24 -- 2.3.3 Stochastic Estimator Learning Automaton 26 -- 2.4 Summary 27 -- 2.5 Exercises 28 -- 3 Fast Learning Automata 31 -- 3.1 Last-position Elimination-based Learning Automata 31 -- 3.1.1 Background and Motivation 32 -- 3.1.2 Principles and Algorithm Design 35 -- 3.1.3 Difference Analysis 37 -- 3.1.4 Simulation Studies 40 -- 3.1.5 Summary 45 -- 3.2 Fast Discretized Pursuit Learning Automata 46 -- 3.2.1 Background and Motivation 46 -- 3.2.2 Algorithm Design of Fast Discretized Pursuit LAs 48 -- 3.2.3 Optimality Analysis 54 -- 3.2.4 Simulation Studies 59 -- 3.2.5 Summary 63 -- 3.3 Exercises 63 -- 4 Application-Oriented Learning Automata 67 -- 4.1 Discovering and Tracking Spatiotemporal Event Patterns 67 -- 4.1.1 Background and Motivation 69 -- 4.1.2 Spatiotemporal Pattern Learning Automata 70 -- 4.1.3 Adaptive Tunable Spatiotemporal Pattern Learning Automata 73 -- 4.1.4 Optimality Analysis 76 -- 4.1.5 Simulation Studies 83 -- 4.1.6 Summary 89 -- 4.2 Stochastic Searching on the Line 89 -- 4.2.1 Background and Motivation 89 -- 4.2.2 Symmetrical Hierarchical Stochastic Searching on the Line 95 -- 4.2.3 Simulation Studies 99 -- 4.2.4 Summary 104 -- 4.3 Fast Adaptive Search on the Line in Dual Environments 104 -- 4.3.1 Background and Motivation 109 -- 4.3.2 Symmetrized ASS with Buffer 111 -- 4.3.3 Simulation Studies 114 -- 4.3.4 Summary 118 -- 4.4 Exercises 118 -- 5 Ordinal Optimization 123 -- 5.1 Optimal Computing-Budget Allocation 123 -- 5.2 Optimal Computing-Budget Allocation for Selection of Best and Worst Designs 125 -- 5.2.1 Background and Motivation 125 -- 5.2.2 Approximate Optimal Simulation Budget Allocation 126 -- 5.2.3 Simulation Studies 138 -- 5.2.4 Summary 150 -- 5.3 Optimal Computing-Budget Allocation for Subset Ranking 151 -- 5.3.1 Background and Motivation 151 -- 5.3.2 Approximate Optimal Simulation Budget Allocation 153 -- 5.3.3 Simulation Studies 159 -- 5.3.4 Summary 167 -- 5.4 Exercises 167 -- 6 Incorporation of Ordinal Optimization into Learning Automata 175 -- 6.1 Background and Motivation 175 -- 6.2 Learning Automata with Optimal Computing Budget Allocation 178 -- 6.3 Proof of Optimality 182 -- 6.4 Simulation Studies 187 -- 6.5 Summary 193 -- 6.6 Exercises 193 -- 7 Noisy Optimization Applications 199 -- 7.1 Background and Motivation 200 -- 7.2 Particle Swarm Optimization 202 -- 7.2.1 Parameters Configurations 203 -- 7.2.2 Topology Structures 203 -- 7.2.3 Hybrid PSO 203 -- 7.2.4 Multiswarm Techniques 204 -- 7.3 Resampling for Noisy Optimization Problems 204 -- 7.4 PSO-Based LA and OCBA 205 -- 7.5 Simulations Studies 209 -- 7.6 Summary 223 -- 7.7 Exercises 224 -- 8 Applications and Future Research Directions of Learning Automata 231 -- 8.1 Summary of Existing Applications 231 -- 8.1.1 Classification 231 -- 8.1.2 Clustering 233 -- 8.1.3 Games 233 -- 8.1.4 Knapsack Problems 234 -- 8.1.5 Decision Problems in Networks 235 -- 8.1.6 Optimization 236 -- 8.1.7 LA Parallelization and Design Ranking 238 -- 8.1.8 Scheduling 240 -- 8.2 Future Research Directions 241 -- 8.3 Exercises 243 -- References 243 -- Index 249.
520 _a"A learning automaton represents an important and powerful tool in the area of reinforcement learning and aims at learning the optimal one that maximizes the probability of being rewarded out of a set of allowable systems, actions, alternatives, candidates, or designs by the interaction with a random environment. During a cycle, an automaton chooses an action and then receives a stochastic response that can be either a reward or penalty from the environment. The action probability vector of choosing the next action is then updated by employing this response. The ability of learning how to choose the optimal action endows learning automata with high adaptability to the environment, thus saving great expense and time to find the optimal one in various difficult stochastic environments."--
_cProvided by publisher.
545 0 _aAbout the Author JunQi Zhang, PhD, is a Full Professor with Tongji University in Shanghai. He has published 10+ papers in IEEE Transactions and 30+ papers in conferences. His current research interests include learning automata, swarm intelligence, swarm robots, multi-agent systems, reinforcement learning, and big data. MengChu Zhou, PhD, is a Distinguished Professor at New Jersey Institute of Technology. He has over 1100 publications including 14 books, 750+ journal papers (600+ in IEEE transactions), 31 patents, and 32 book-chapters. He is Fellow of IEEE, IFAC, AAAS, CAA and NAI.
650 0 _aMachine theory.
_0https://id.loc.gov/authorities/subjects/sh85079341.
655 4 _aElectronic books.
700 1 _aZhou, MengChu,
_0https://id.loc.gov/authorities/names/n92108716
_eauthor.
856 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781394188536
_yFull text is available at Wiley Online Library Click here to view
942 _2ddc
_cER